Multi-Agent Support Automation

The Problem

Support teams spend 60-70% of their time on repetitive questions while complex issues wait. The cost isn't just inefficiency - it's frustrated customers and burned-out agents.

Why Multi-Agent

Support automation requires distinct optimization targets. The router needs classification accuracy. The resolution agent needs response quality. The confidence gate needs safety validation. Multi-agent architecture lets each component have specialized prompts and independent failure modes.

How It Works

1
Incoming ticket is analyzed by the Triage Agent to classify urgency and topic
2
Low-urgency tickets route to RAG Agent, which retrieves relevant knowledge base articles and drafts a response
3
Confidence check evaluates the AI response - uncertain answers get flagged for human review
4
High-urgency tickets route directly to humans with full context and suggested actions
5
Executive dashboard tracks resolution rates, escalation patterns, and AI confidence scores

Architecture Decisions

Component Technology Purpose
Orchestration n8n Workflow automation connecting all agents and routing logic
Triage Agent Claude Sonnet 4 Classifies urgency, extracts intent, determines routing path
Resolution Agent GPT-4 + Vector DB Retrieves knowledge and generates responses with self-assessment
Confidence Gate Python + Semantic Validation Multi-layer checks: retrieval confidence, response completeness, semantic similarity
Human Routing n8n Escalates uncertain or urgent tickets with full context
Executive Dashboard Looker Studio Tracks metrics, patterns, and system health

Human-in-the-Loop by Design: The system never sends uncertain responses to customers. When confidence scores fall below thresholds (retrieval confidence, semantic validation, or self-assessment), it routes to human review with diagnostic data.

Why Claude for Triage, GPT-4 for Resolution? Claude excels at structured classification. GPT-4's native file search made RAG faster to implement. Right model for each task beats one-size-fits-all.

What I Learned

  • 💡 Multi-layer confidence validation is critical. Responses go through retrieval confidence scoring, semantic validation against source docs, and self-assessment. This catches both uncertain responses and confident hallucinations before they reach customers.
  • 💡 Escalation with context. Human support teams see classification, urgency, and sentiment upfront. They know immediately if it's an angry billing dispute or technical question.
  • 💡 Multi-LLM beats single model. Claude for classification, GPT-4 for resolution. Each excels at its task. Specialized components beat one-size-fits-all.